Analyzing PyPI package download statistics and building a data application

In this article, I will try to give you an overview of how one can build an interactive data application with dstack. We will also learn how to analyze and visualize data with Python, pandas, and plot.ly, and query data from Google’s BigQuery using Python and pandas.

Even though Jupyter notebooks are indispensable to writing code and interactive engagement with data, there is still a need to build applications for non-technical audiences. Fortunately, the Python ecosystem offers a wide and vibrant open-source ecosystem not only for data analysis but also for building data applications using the data and visualization libraries.

In this article, I will try to give you an overview of how one can build an interactive data application with dstack. We will also learn how to analyze and visualize data with Python, pandas, and plot.ly, and query data from Google’s BigQuery using Python and pandas.

For the sake of education, we will analyze the download statistics for Python packages and make our application visualize the following:

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